Public Health Students' Perceptions of AI Technology in Education and Practice
Bibliographic record
Abstract
This thesis examines the perceptions of public health students regarding Artificial Intelligence (AI) technology in their education and future practice. As AI rapidly transforms healthcare and public health, there is a growing need to understand the preparedness and perspectives of the emerging public health workforce. This study addresses the research question: What are public health students' perceptions of AI technology in their education and practice? Employing a qualitative descriptive methodology, this research conducted focus group interviews with 24 Master of Public Health (MPH) and Master of Science (MSc) students from the University of Alberta. Data were analyzed using reflexive thematic analysis. Findings reveal public health students possess a nuanced understanding of AI, marked by curiosity, excitement, and uncertainty. They actively use tools like ChatGPT for academic writing, research, and personalized learning, valuing the efficiency and recognizing AI's potential in epidemiological surveillance, disease diagnosis, and health management. However, students raised significant concerns regarding data privacy, algorithmic bias exacerbating health inequities, misinformation eroding public trust, the ethics of AI influencing behavior change, AI's deficient emotional/cultural intelligence, and the risk of overreliance diminishing critical thinking. To mitigate these, they suggested fostering community engagement in AI development, adopting multidisciplinary approaches to reduce bias, ensuring robust human oversight, and developing comprehensive, ethics-focused AI curricula that address generational and digital gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".